We propose a simple yet effective metric that measures structural similarity
between visual instances of architectural floor plans, without the need for
learning. Qualitatively, our experiments show that the retrieval results are
similar to deeply learned methods. Effectively comparing instances of floor
plan data is paramount to the success of machine understanding of floor plan
data, including the assessment of floor plan generative models and floor plan
recommendation systems. Comparing visual floor plan images goes beyond a sole
pixel-wise visual examination and is crucially about similarities and
differences in the shapes and relations between subdivisions that compose the
layout. Currently, deep metric learning approaches are used to learn a
pair-wise vector representation space that closely mimics the structural
similarity, in which the models are trained on similarity labels that are
obtained by Intersection-over-Union (IoU). To compensate for the lack of
structural awareness in IoU, graph-based approaches such as Graph Matching
Networks (GMNs) are used, which require pairwise inference for comparing data
instances, making GMNs less practical for retrieval applications. In this
paper, an effective evaluation metric for judging the structural similarity of
floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed
based on both image and graph distances. In addition, an efficient algorithm is
developed that uses SSIG to rank a large-scale floor plan database. Code will
be openly available.

提出了一种基于图像和图形距离的评估指标 SSIG（基于 IoU 和 GED 的结构相似性），用于判断平面图的结构相似性，并开发了一种有效的算法，通过 SSIG 对大规模的平面图数据库进行排序。